Includes top ten must know machine learning methods with R.

Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning

mlim: single and multiple imputation with automated machine learning

In this work, the application of the Triple-Barrier Method and Meta-Labeling techniques are explored using XGBoost to develop a sentiment-based trading signal for the S&P 500 stock market index. The results indicate that sentiment data possess predictive power; however, substantial work remains before a fully implementable strategy can be realized.

Solution for the Ultimate Student Hunt Challenge (1st place).

In this project we will be using the publicly available and Kaggle-popular LendingClub data set to train Linear Regression and Extreme Gradient Descent Boosted Decision Tree models to predict interest rates assigned to loans. First, we will clean and prepare the data. This includes feature removal, feature engineering, and string processing.There are several entries where values have been deleted to simulate dirty data. Then, we will build machine learning models in Python to predict the interest rates assigned to loans. We will evaluate our models' performances using the root mean squared error (RMSE) metric and compare our models' results.

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

This repository contains several machine learning projects done in Jupyter Notebooks

Integrative Biomechanical and Clinical Features Predict In-Hospital Trauma Mortality

Comparison of ensemble learning methods on diabetes disease classification with various datasets

This repo contains the result of my computer science course: An automated tool to classify credit card transactions. Could be used with any dataset

Sports Analytics in R (Gradient Boost approaches for Decision Tree in Regression problems)

Kaggle challenge to predict if a customer is satisfied or dissatisfied with their banking experience.

Code for the project "Predicting hospital readmission of diabetic patients using ensemble learning"

Kaggle challenge asking to predict how a supplier will quote a price on a given tube assembly.

Kaggle challenge asking to predict the outcome for each animal of the shelter.

Using data to help us choice high quality wine

Kaggle challenge asking to predict the final price of each home based on their description/properties.

Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China

Applies Machine Learning approach to predict spam.

Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling

This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.

Credit Card Fraud Detection using Extreme Gradient Boosting

Algorithms used to confirm whether a celestial body is a planet or not.

Big Mart Sales Prediction is a data-driven project aiming to forecast product sales accurately across Big Mart outlets. Leveraging machine learning and comprehensive datasets, our project empowers retailers to optimize inventory, enhance profitability, and make informed decisions in the dynamic world of retail.

Digital image processing notebooks for CSci 142 - Graphics and Visual Computing

Predicting true low-VAF SNVs in HPV using triplicate NGS samples and machine learning

🔍 Clean and analyze datasets while building predictive models with Decision Trees, enhancing skills in data science and analytics.